Compressive sensing method for EUV source optimization using different bases

An EUV source optimization technique using compressive sensing is introduced in this paper. The pixelated source pattern is sparsely represented in a set of certain basis functions. Blue noise sampling method is used to select sampling points around the margins of the target layout for imaging fidelity evaluation. Based on the compressive sensing theory, the EUV SO is formulated as an l1-norm inverse reconstruction problem and solved by the linearized Bregman algorithm. Different types of sparse bases are also experimented in this paper to investigate their impact on the SO results. These bases include the 2D-DCT basis, spatial basis, Zernike basis, and Haar wavelet basis. Simulations show that ethe Haar wavelet basis results in the best imaging fidelity among the four types of bases.